CONSTRAINT INFORMATIVE RULES FOR GENETIC ALGORITHM-BASED WEB PAGE RECOMMENDATION SYSTEM

Abstract

To predict the users navigation using web usage mining is the primary motto of the web page recommendation. Currently, researchers are trying to develop a web page recommendation using pattern mining technique. Here, we propose a technique for web page recommendation using genetic algorithm. It consists of three phases as data preparation, mining of informative rules and recommendation. The data preparation contains data preprocessing and user identification. The genetic algorithm is used to mine the informative rule. The genetic algorithm involves three processes which are calculating the fitness values, crossover and mutation. We use three different constraints as time duration, quality and recent visit to allow the process for next stage after the initial fitness calculation. We have to repeat these processes to find the best solution. To form the recommendation tree, we use the best solution which we obtain by means of genetic algorithm.